For example, the Patent Application DE 10 2014 210 279 A1 is from the related art. This patent relates to a method for detecting the state of attention of the driver of a vehicle, in which the time characteristic of the vehicle speed is compared to a reference characteristic, and a signal corresponding to a reduced state of attention is generated if the time characteristic of the vehicle speed deviates by a defined amount from the reference characteristic. In this case, it is also mentioned that the gestures, facial expressions or the eye behavior of the driver is/are taken into account, a signal corresponding to a reduced state of attention being generated if the gestures, facial expressions or eye behavior point to behavior typical for drowsiness.
The document DE 10126224 A1 is from the related art. This document relates to a method and a device for its implementation, in order to characterize the state of the driver of a motor vehicle. A driver-state monitor determines variables concerning the physiological state of the driver. Depending on the ascertained result, control units of the motor vehicle are acted upon, as well as the driver, in order to implement measures suitable for maintaining a safe state of the driver and of the motor vehicle. As physiological state variables, in particular, brain waves are determined by electroencephalogram, the condition of the heart is determined by electrocardiogram, and blood pressure, heart rate or pulse, heart motion, skin temperature and conductance of the skin are determined and possibly stored. The eye-blinking rate, the force with which the driver grips the steering wheel and his movements on the seat may be determined as further key parameters, evaluated in relevant manner, and combined with the other results in order to initiate measures capable of influencing the vehicle and driver.
In existing systems for assessing sleepiness, in order to estimate the sleepiness (sleepiness and drowsiness are used as synonyms within the context of this patent application), attention is paid to specific features of individual blinking events like, for example, the amplitude or the speed of the lid movement. In so doing, in each case the values for blinking events within one specific period of time are utilized to assess the sleepiness. The input signals used for the regression of the sleepiness may represent blinking-related values like, e.g., the blinking amplitude or duration. However, other values calculated from the eyelid-opening signal are also used, such as the PERCLOS value—the eye opening averaged over a certain period of time of, e.g., 60 seconds.
In existing systems for assessing the sleepiness of a driver, the assessment is based solely on the instantaneously available features such as PERCLOS or blinking-related features, for example, calculated from an eyelid-opening signal. In some versions, the input values are also averaged over a certain period of time. However, it remains the case that an estimated sleepiness value is always determined from the input quantities of one specific period of time. As a result, the respective next estimated sleepiness value does not refer to the previous period of time.
In this context, it is not considered that as a rule, sleepiness does not develop suddenly. Nevertheless, in principle, there is the possibility that the output value of the sleepiness estimate could make big leaps, for example, on the basis of a specific situation measured at certain points in the case of the driver, or perhaps because of possible measuring inaccuracies or measuring errors.